C=1,
tol=.01,
inactive_threshold=1e-3,
batch_size=10)
latent_svm = LatentSSVM(base_ssvm=base_ssvm, latent_iter=2)
latent_svm.fit(X_train_, y_train)
print("Score with binary SVM:")
print("Train: {:2.2f}".format(svm.score(X_train_, y_train)))
print("Test: {:2.2f}".format(svm.score(X_test_, y_test)))
print("Score with latent SVM:")
print("Train: {:2.2f}".format(latent_svm.score(X_train_, y_train)))
print("Test: {:2.2f}".format(latent_svm.score(X_test_, y_test)))
h_pred = np.hstack(latent_svm.predict_latent(X_test_))
print("Latent class counts: %s" % repr(np.bincount(h_pred)))
# plot first few digits from each latent class
plt.figure(figsize=(3, 5))
plt.suptitle("Example digits from each of\nthe ten latent classes.")
n_latent_classes = 10
n_examples = 7
for latent_class in xrange(n_latent_classes):
examples = X_test[h_pred == latent_class][:n_examples]
for k, example in enumerate(examples):
plt.subplot(n_latent_classes, n_examples,
1 + (n_examples * latent_class + k))
plt.imshow(example.reshape((8, 8)), cmap=plt.cm.gray_r)
plt.xticks(())
show_loss_every=10,
inference_cache=50)
latent_svm = LatentSSVM(ssvm)
# make edges for hidden states:
edges = []
node_indices = np.arange(4 * 4).reshape(4, 4)
for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)):
for j in xrange(x, x + 2):
for k in xrange(y, y + 2):
edges.append([i + 4 * 4, node_indices[j, k]])
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# Random initialization
H_init = [
np.hstack([y.ravel(), np.random.randint(2, 4, size=2 * 2)]) for y in Y
]
plot_boxes(H_init,
title="Top: Random initial hidden states. Bottom: Ground"
"truth labeling.")
X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
latent_svm.fit(X_, Y_flat, H_init)
print("Training score with latent nodes: %f " % latent_svm.score(X_, Y_flat))
H = latent_svm.predict_latent(X_)
plot_boxes(H, title="Top: Hidden states after training. Bottom: Prediction.")
plt.show()
inference_method='lp')
ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100,
n_jobs=-1, show_loss_every=10, inference_cache=50)
latent_svm = LatentSSVM(ssvm)
# make edges for hidden states:
edges = []
node_indices = np.arange(4 * 4).reshape(4, 4)
for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)):
for j in range(x, x + 2):
for k in range(y, y + 2):
edges.append([i + 4 * 4, node_indices[j, k]])
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# Random initialization
H_init = [np.hstack([y.ravel(), np.random.randint(2, 4, size=2 * 2)])
for y in Y]
plot_boxes(H_init, title="Top: Random initial hidden states. Bottom: Ground"
"truth labeling.")
X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat]))
latent_svm.fit(X_, Y_flat, H_init)
print("Training score with latent nodes: %f " % latent_svm.score(X_, Y_flat))
H = latent_svm.predict_latent(X_)
plot_boxes(H, title="Top: Hidden states after training. Bottom: Prediction.")
plt.show()
latent_pbl = LatentGraphCRF(n_states_per_label=5,
inference_method='unary')
base_ssvm = NSlackSSVM(latent_pbl, C=1, tol=.01,
inactive_threshold=1e-3, batch_size=10)
latent_svm = LatentSSVM(base_ssvm=base_ssvm, latent_iter=2)
latent_svm.fit(X_train_, y_train)
print("Score with binary SVM:")
print("Train: {:2.2f}".format(svm.score(X_train_, y_train)))
print("Test: {:2.2f}".format(svm.score(X_test_, y_test)))
print("Score with latent SVM:")
print("Train: {:2.2f}".format(latent_svm.score(X_train_, y_train)))
print("Test: {:2.2f}".format(latent_svm.score(X_test_, y_test)))
h_pred = np.hstack(latent_svm.predict_latent(X_test_))
print("Latent class counts: %s" % repr(np.bincount(h_pred)))
# plot first few digits from each latent class
plt.figure(figsize=(3, 5))
plt.suptitle("Example digits from each of\nthe ten latent classes.")
n_latent_classes = 10
n_examples = 7
for latent_class in range(n_latent_classes):
examples = X_test[h_pred == latent_class][:n_examples]
for k, example in enumerate(examples):
plt.subplot(n_latent_classes, n_examples,
1 + (n_examples * latent_class + k))
plt.imshow(example.reshape((8, 8)), cmap=plt.cm.gray_r)
plt.xticks(())
